2021

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20 pages, 2469 KiB

Open AccessArticle

Advances in Non-Invasive Blood Pressure Monitoring

by Xina Quan, Junjun Liu, Thomas Roxlo, Siddharth Siddharth, Weyland Leong, Arthur Muir, So-Min Cheong and Anoop Rao

Sensors 2021, 21(13), 4273; https://doi.org/10.3390/s21134273 - 22 Jun 2021

Cited by 26 | Viewed by 33792

Abstract

This paper reviews recent advances in non-invasive blood pressure monitoring and highlights the added value of a novel algorithm-based blood pressure sensor which uses machine-learning techniques to extract blood pressure values from the shape of the pulse waveform. We report results from preliminary [...] Read more.

This paper reviews recent advances in non-invasive blood pressure monitoring and highlights the added value of a novel algorithm-based blood pressure sensor which uses machine-learning techniques to extract blood pressure values from the shape of the pulse waveform. We report results from preliminary studies on a range of patient populations and discuss the accuracy and limitations of this capacitive-based technology and its potential application in hospitals and communities. Full article

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27 pages, 506 KiB

Open AccessReview

Intelligent Sensing Technologies for the Diagnosis, Monitoring and Therapy of Alzheimer’s Disease: A Systematic Review

by Nazia Gillani and Tughrul Arslan

Sensors 2021, 21(12), 4249; https://doi.org/10.3390/s21124249 - 21 Jun 2021

Cited by 24 | Viewed by 4798

Abstract

Alzheimer’s disease is a lifelong progressive neurological disorder. It is associated with high disease management and caregiver costs. Intelligent sensing systems have the capability to provide context-aware adaptive feedback. These can assist Alzheimer’s patients with, continuous monitoring, functional support and timely therapeutic interventions [...] Read more.

Alzheimer’s disease is a lifelong progressive neurological disorder. It is associated with high disease management and caregiver costs. Intelligent sensing systems have the capability to provide context-aware adaptive feedback. These can assist Alzheimer’s patients with, continuous monitoring, functional support and timely therapeutic interventions for whom these are of paramount importance. This review aims to present a summary of such systems reported in the extant literature for the management of Alzheimer’s disease. Four databases were searched, and 253 English language articles were identified published between the years 2015 to 2020. Through a series of filtering mechanisms, 20 articles were found suitable to be included in this review. This study gives an overview of the depth and breadth of the efficacy as well as the limitations of these intelligent systems proposed for Alzheimer’s. Results indicate two broad categories of intelligent technologies, distributed systems and self-contained devices. Distributed systems base their outcomes mostly on long-term monitoring activity patterns of individuals whereas handheld devices give quick assessments through touch, vision and voice. The review concludes by discussing the potential of these intelligent technologies for clinical practice while highlighting future considerations for improvements in the design of these solutions for Alzheimer’s disease. Full article

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11 pages, 3359 KiB

Open AccessArticle

Cuffless Blood Pressure Measurement Using a Smartphone-Case Based ECG Monitor with Photoplethysmography in Hypertensive Patients

by Zhanna Sagirova, Natalia Kuznetsova, Nana Gogiberidze, Daria Gognieva, Aleksandr Suvorov, Petr Chomakhidze, Stefano Omboni, Hugo Saner and Philippe Kopylov

Sensors 2021, 21(10), 3525; https://doi.org/10.3390/s21103525 - 19 May 2021

Cited by 28 | Viewed by 4366

Abstract

The availability of simple, accurate, and affordable cuffless blood pressure (BP) devices has the potential to greatly increase the compliance with measurement recommendations and the utilization of BP measurements for BP telemonitoring. The aim of this study is to evaluate the correlation between [...] Read more.

The availability of simple, accurate, and affordable cuffless blood pressure (BP) devices has the potential to greatly increase the compliance with measurement recommendations and the utilization of BP measurements for BP telemonitoring. The aim of this study is to evaluate the correlation between findings from routine BP measurements using a conventional sphygmomanometer with the results from a portable ECG monitor combined with photoplethysmography (PPG) for pulse wave registration in patients with arterial hypertension. Methods: The study included 500 patients aged 32–88 years (mean 64 ± 7.9 years). Mean values from three routine BP measurements by a sphygmomanometer with cuff were selected for comparison; within one minute after the last measurement, an electrocardiogram (ECG) was recorded for 3 min in the standard lead I using a smartphone-case based single-channel ECG monitor (CardioQVARK^®-limited responsibility company “L-CARD”, Moscow, Russia) simultaneously with a PPG pulse wave recording. Using a combination of the heart signal with the PPG, levels of systolic and diastolic BP were determined based on machine learning using a previously developed and validated algorithm and were compared with sphygmomanometer results. Results: According to the Bland–Altman analysis, SD for systolic BP was 3.63, and bias was 0.32 for systolic BP. SD was 2.95 and bias was 0.61 for diastolic BP. The correlation between the results from the sphygmomanometer and the cuffless method was 0.89 (p = 0.001) for systolic and 0.87 (p = 0.002) for diastolic BP. Conclusion: Blood pressure measurements on a smartphone-case without a cuff are encouraging. However, further research is needed to improve the accuracy and reliability of clinical use in the majority of patients. Full article

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14 pages, 2835 KiB

Open AccessArticle

Sensor Validation and Diagnostic Potential of Smartwatches in Movement Disorders

by Julian Varghese, Catharina Marie van Alen, Michael Fujarski, Georg Stefan Schlake, Julitta Sucker, Tobias Warnecke and Christine Thomas

Sensors 2021, 21(9), 3139; https://doi.org/10.3390/s21093139 - 30 Apr 2021

Cited by 14 | Viewed by 3811

Abstract

Smartwatches provide technology-based assessments in Parkinson’s disease (PD). It is necessary to evaluate their reliability and accuracy in order to include those devices in an assessment. We present unique results for sensor validation and disease classification via machine learning (ML). A comparison setup [...] Read more.

Smartwatches provide technology-based assessments in Parkinson’s disease (PD). It is necessary to evaluate their reliability and accuracy in order to include those devices in an assessment. We present unique results for sensor validation and disease classification via machine learning (ML). A comparison setup was designed with two different series of Apple smartwatches, one Nanometrics seismometer and a high-precision shaker to measure tremor-like amplitudes and frequencies. Clinical smartwatch measurements were acquired from a prospective study including 450 participants with PD, differential diagnoses (DD) and healthy participants. All participants wore two smartwatches throughout a 15-min examination. Symptoms and medical history were captured on the paired smartphone. The amplitude error of both smartwatches reaches up to 0.005 g, and for the measured frequencies, up to 0.01 Hz. A broad range of different ML classifiers were cross-validated. The most advanced task of distinguishing PD vs. DD was evaluated with 74.1% balanced accuracy, 86.5% precision and 90.5% recall by Multilayer Perceptrons. Deep-learning architectures significantly underperformed in all classification tasks. Smartwatches are capable of capturing subtle tremor signs with low noise. Amplitude and frequency differences between smartwatches and the seismometer were under the level of clinical significance. This study provided the largest PD sample size of two-hand smartwatch measurements and our preliminary ML-evaluation shows that such a system provides powerful means for diagnosis classification and new digital biomarkers, but it remains challenging for distinguishing similar disorders. Full article

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22 pages, 27380 KiB

Open AccessArticle

Noninvasive Measurement of Tongue Pressure and Its Correlation with Swallowing and Respiration

by Wann-Yun Shieh, Chin-Man Wang, Hsin-Yi Kathy Cheng and Titilianty Ignatia Imbang

Sensors 2021, 21(8), 2603; https://doi.org/10.3390/s21082603 - 07 Apr 2021

Cited by 6 | Viewed by 3023

Abstract

Tongue pressure plays a critical role in the oral and pharyngeal stages of swallowing, contributing considerably to bolus formation and manipulation as well as to safe transporting of food from the mouth to the stomach. Smooth swallowing relies not only on effective coordination [...] Read more.

Tongue pressure plays a critical role in the oral and pharyngeal stages of swallowing, contributing considerably to bolus formation and manipulation as well as to safe transporting of food from the mouth to the stomach. Smooth swallowing relies not only on effective coordination of respiration and pharynx motions but also on sufficient tongue pressure. Conventional methods of measuring tongue pressure involve attaching a pressure sheet to the hard palate to monitor the force exerted by the tongue tip against the hard palate. In this study, an air bulb was inserted in the anterior oral cavity to monitor the pressure exerted by the extrinsic and intrinsic muscles of the tongue. The air bulb was integrated into a noninvasive, multisensor approach to evaluate the correlation of the tongue pressure with other swallowing responses, such as respiratory nasal flow, submental muscle movement, and thyroid cartilage excursion. An autodetection program was implemented for the automatic identification of swallowing patterns and parameters from each sensor. The experimental results indicated that the proposed method is sensitive in measuring the tongue pressure, and the tongue pressure was found to have a strong positive correlation with the submental muscle movement during swallowing. Full article

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16 pages, 20959 KiB

Open AccessArticle

Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

by Jaewon Lee, Hyeonjeong Lee and Miyoung Shin

Sensors 2021, 21(7), 2381; https://doi.org/10.3390/s21072381 - 30 Mar 2021

Cited by 21 | Viewed by 4462

Abstract

Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving [...] Read more.

Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s). Full article

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18 pages, 6415 KiB

Open AccessArticle

Prediction of Motion Intentions as a Novel Method of Upper Limb Rehabilitation Support

by Bogusz Lewandowski, Sławomir Wudarczyk, Przemysław Sperzyński and Jacek Bałchanowski

Sensors 2021, 21(2), 410; https://doi.org/10.3390/s21020410 - 08 Jan 2021

Cited by 2 | Viewed by 2163

Abstract

This article is devoted to the novel method of upper limb rehabilitation support using a dedicated mechatronic system. The mechatronic rehabilitation system’s main advantages are the repeatability of the process and the ability to measure key features and the progress of the therapy. [...] Read more.

This article is devoted to the novel method of upper limb rehabilitation support using a dedicated mechatronic system. The mechatronic rehabilitation system’s main advantages are the repeatability of the process and the ability to measure key features and the progress of the therapy. In addition, the assisted therapy standard is the same for each patient. The new method proposed in this article is based on the prediction of the patient’s intentions, understood as the intentions to perform a movement that would be not normally possible due to the patient’s limited motor functions. Determining those intentions is realized based on a comparative analysis of measured kinematic (range of motion, angular velocities, and accelerations) and dynamic parameter values, as well as external loads resulting from the interaction of patients. Appropriate procedures were implemented in the control system, for which verification was conducted via experiments. The aim of the research in the article was to examine whether it is possible to sense the movement intentions of a patient during exercises, using only measured load parameters and kinematic parameters of the movement. In this study, the construction of a mechatronic system prototype equipped with sensory grip to measure the external loads, control algorithms, and the description of experimental studies were presented. The experimental studies of the mechanism were aimed at the verification of the proper operation of the system and were not a clinical trial. Full article

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2020

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11 pages, 223 KiB

Open AccessPerspective

Digital Health in Cardiac Rehabilitation and Secondary Prevention: A Search for the Ideal Tool

by Maarten Falter, Martijn Scherrenberg and Paul Dendale

Sensors 2021, 21(1), 12; https://doi.org/10.3390/s21010012 - 22 Dec 2020

Cited by 20 | Viewed by 4563

Abstract

Digital health is becoming more integrated in daily medical practice. In cardiology, patient care is already moving from the hospital to the patients’ homes, with large trials showing positive results in the field of telemonitoring via cardiac implantable electronic devices (CIEDs), monitoring of [...] Read more.

Digital health is becoming more integrated in daily medical practice. In cardiology, patient care is already moving from the hospital to the patients’ homes, with large trials showing positive results in the field of telemonitoring via cardiac implantable electronic devices (CIEDs), monitoring of pulmonary artery pressure via implantable devices, telemonitoring via home-based non-invasive sensors, and screening for atrial fibrillation via smartphone and smartwatch technology. Cardiac rehabilitation and secondary prevention are modalities that could greatly benefit from digital health integration, as current compliance and cardiac rehabilitation participation rates are low and optimisation is urgently required. This viewpoint offers a perspective on current use of digital health technologies in cardiac rehabilitation, heart failure and secondary prevention. Important barriers which need to be addressed for implementation in medical practice are discussed. To conclude, a future ideal digital tool and integrated healthcare system are envisioned. To overcome personal, technological, and legal barriers, technological development should happen in dialog with patients and caregivers. Aided by digital technology, a future could be realised in which we are able to offer high-quality, affordable, personalised healthcare in a patient-centred way. Full article

17 pages, 9823 KiB

Open AccessArticle

Backward Walking Induces Significantly Larger Upper-Mu-Rhythm Suppression Effects Than Forward Walking Does

by Nan-Hung Lin, Chin-Hsuan Liu, Posen Lee, Lan-Yuen Guo, Jia-Li Sung, Chen-Wen Yen and Lih-Jiun Liaw

Sensors 2020, 20(24), 7250; https://doi.org/10.3390/s20247250 - 17 Dec 2020

Cited by 9 | Viewed by 3092

Abstract

Studies have compared the differences and similarities between backward walking and forward walking, and demonstrated the potential of backward walking for gait rehabilitation. However, current evidence supporting the benefits of backward walking over forward walking remains inconclusive. Considering the proven association between gait [...] Read more.

Studies have compared the differences and similarities between backward walking and forward walking, and demonstrated the potential of backward walking for gait rehabilitation. However, current evidence supporting the benefits of backward walking over forward walking remains inconclusive. Considering the proven association between gait and the cerebral cortex, we used electroencephalograms (EEG) to differentiate the effects of backward walking and forward walking on cortical activities, by comparing the sensorimotor rhythm (8–12 Hz, also called mu rhythm) of EEG signals. A systematic signal procedure was used to eliminate the motion artifacts induced by walking to safeguard EEG signal fidelity. Statistical test results of our experimental data demonstrated that walking motions significantly suppressed mu rhythm. Moreover, backward walking exhibited significantly larger upper mu rhythm (10–12 Hz) suppression effects than forward walking did. This finding implies that backward walking induces more sensorimotor cortex activity than forward walking does, and provides a basis to support the potential benefits of backward walking over forward walking. By monitoring the upper mu rhythm throughout the rehabilitation process, medical experts can adaptively adjust the intensity and duration of each walking training session to improve the efficacy of a walking ability recovery program. Full article

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19 pages, 3408 KiB

Open AccessArticle

Feasibility of Heart Rate and Respiratory Rate Estimation by Inertial Sensors Embedded in a Virtual Reality Headset

by Claudia Floris, Sarah Solbiati, Federica Landreani, Gianfranco Damato, Bruno Lenzi, Valentino Megale and Enrico Gianluca Caiani

Sensors 2020, 20(24), 7168; https://doi.org/10.3390/s20247168 - 14 Dec 2020

Cited by 14 | Viewed by 6632

Abstract

Virtual reality (VR) headsets, with embedded micro-electromechanical systems, have the potential to assess the mechanical heart’s functionality and respiratory activity in a non-intrusive way and without additional sensors by utilizing the ballistocardiographic principle. To test the feasibility of this approach for opportunistic physiological [...] Read more.

Virtual reality (VR) headsets, with embedded micro-electromechanical systems, have the potential to assess the mechanical heart’s functionality and respiratory activity in a non-intrusive way and without additional sensors by utilizing the ballistocardiographic principle. To test the feasibility of this approach for opportunistic physiological monitoring, thirty healthy volunteers were studied at rest in different body postures (sitting (SIT), standing (STAND) and supine (SUP)) while accelerometric and gyroscope data were recorded for 30 s using a VR headset (Oculus Go, Oculus, Microsoft, USA) simultaneously with a 1-lead electrocardiogram (ECG) signal for mean heart rate (HR) estimation. In addition, longer VR acquisitions (50 s) were performed under controlled breathing in the same three postures to estimate the respiratory rate (RESP). Three frequency-based methods were evaluated to extract from the power spectral density the corresponding frequency. By the obtained results, the gyroscope outperformed the accelerometer in terms of accuracy with the gold standard. As regards HR estimation, the best results were obtained in SIT, with R_s² (95% confidence interval) = 0.91 (0.81−0.96) and bias (95% Limits of Agreement) −1.6 (5.4) bpm, followed by STAND, with R_s² = 0.81 (0.64−0.91) and −1.7 (11.6) bpm, and SUP, with R_s² = 0.44 (0.15−0.68) and 0.2 (19.4) bpm. For RESP rate estimation, SUP showed the best feasibility (98%) to obtain a reliable value from each gyroscope axis, leading to the identification of the transversal direction as the one containing the largest breathing information. These results provided evidence of the feasibility of the proposed approach with a degree of performance and feasibility dependent on the posture of the subject, under the conditions of keeping the head still, setting the grounds for future studies in real-world applications of HR and RESP rate measurement through VR headsets. Full article

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18 pages, 9676 KiB

Open AccessArticle

Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning

by Luca Antognoli, Sara Moccia, Lucia Migliorelli, Sara Casaccia, Lorenzo Scalise and Emanuele Frontoni

Sensors 2020, 20(18), 5362; https://doi.org/10.3390/s20185362 - 18 Sep 2020

Cited by 13 | Viewed by 4924

Abstract

Background: Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting heartbeat from the carotid [...] Read more.

Background: Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting heartbeat from the carotid LDV signal. Methods: The performances of Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN) were compared using the leave-one-subject-out cross-validation as the testing protocol in an LDV dataset collected from 28 subjects. The classification was conducted on LDV signal windows, which were labeled as beat, if containing a beat, or no-beat, otherwise. The labeling procedure was performed using electrocardiography as the gold standard. Results: For the beat class, the f1-score (

f 1

) values were 0.93, 0.93, 0.95, 0.96 for RF, DT, KNN and SVM, respectively. No statistical differences were found between the classifiers. When testing the SVM on the full-length (10 min long) LDV signals, to simulate a real-world application, we achieved a median macro-

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of 0.76. Conclusions: Using machine learning for heartbeat detection from carotid LDV signals showed encouraging results, representing a promising step in the field of contactless cardiovascular signal analysis. Full article

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12 pages, 243 KiB

Open AccessArticle

Psychophysiological Models to Identify and Monitor Elderly with a Cardiovascular Condition: The Added Value of Psychosocial Parameters to Routinely Applied Physiological Assessments

by Victor Kallen, Jan Willem Marck, Jacqueline Stam, Amine Issa, Bruce Johnson and Nico van Meeteren

Sensors 2020, 20(11), 3240; https://doi.org/10.3390/s20113240 - 07 Jun 2020

Cited by 3 | Viewed by 2439

Abstract

The steadily growing elderly population calls for efficient, reliable and preferably ambulant health supervision. Since cardiovascular risk factors interact with psychosocial strain (e.g., depression), we investigated the potential contribution of psychosocial factors in discriminating generally healthy elderly from those with a cardiovascular condition, [...] Read more.

The steadily growing elderly population calls for efficient, reliable and preferably ambulant health supervision. Since cardiovascular risk factors interact with psychosocial strain (e.g., depression), we investigated the potential contribution of psychosocial factors in discriminating generally healthy elderly from those with a cardiovascular condition, on and above routinely applied physiological assessments. Fifteen elderly (aged 60 to 88) with a cardiovascular diagnosis were compared to fifteen age and gender matched healthy peers. Six sequential standardized lab assessments were conducted (one every two weeks), including an autonomic test battery, a 6-min step test and questionnaires covering perceived psychological state and experiences over the previous two weeks. Specific combinations of physiological and psychological factors (most prominently symptoms of depression) effectively predicted (clinical) cardiovascular markers. Additionally, a highly significant prognostic model was found, including depressive symptoms, recently experienced negative events and social isolation. It appeared slightly superior in identifying elderly with or without a cardiovascular condition compared to a model that only included physiological parameters. Adding psychosocial parameters to cardiovascular assessments in elderly may consequently provide protocols that are significantly more efficient, relatively comfortable and technologically feasible in ambulant settings, without necessarily compromising prognostic accuracy. Full article

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